Performance evaluation of whey flux in dead-end and cross-flow modes via convolutional neural networks

The intelligent prediction of membrane fouling during membrane filtration by mathematical models and artificial intelligence models

Recently, membrane separation technology has been widely utilized in filtration process intensification due to its efficient performance and unique advantages, but membrane fouling limits its development and application. Therefore, the research on membrane fouling prediction and control technology is crucial to effectively reduce membrane fouling and improve separation performance. This review first introduces the main factors (operating condition, material characteristics, and membrane structure properties) and the corresponding principles that affect membrane fouling. In addition, mathematical models (Hermia model and Tandem resistance model), artificial intelligence (AI) models (Artificial neural networks model and fuzzy control model), and AI optimization methods (genetic algorithm and particle swarm algorithm), which are widely used for the prediction of membrane fouling, are summarized and analyzed for comparison. The AI models are usually significantly better than the mathematical models in terms of prediction accuracy and applicability of membrane fouling and can monitor membrane fouling in real-time by working in concert with image processing technology, which is crucial for membrane fouling prediction and mechanism studies. Meanwhile, AI models for membrane fouling prediction in the separation process have shown good potential and are expected to be further applied in large-scale industrial applications for separation and filtration process intensification. This review will help researchers understand the challenges and future research directions in membrane fouling prediction, which is expected to provide an effective method to reduce or even solve the bottleneck problem of membrane fouling, and to promote the further application of AI modeling in environmental and food fields.

Potential Future Directions in Optimization of Students' Performance Prediction System

Previous studies widely report the optimization of performance predictions to highlight at-risk students and advance the achievement of excellent students. They also have contributions that overlap different fields of research. On the one hand, they have insightful psychological studies, data mining discoveries, and data analysis findings. On the other hand, they produce a variety of performance prediction approaches to assess students' performance during cognitive tasks. However, the synchronization between these studies is still a black box that increases prediction systems' dependency on real-world datasets. It also delays the mathematical modeling of students' emotional attributes. This review paper performs an insightful analysis and thorough literature-based survey to draw a comprehensive picture of potential challenges and prior contributions. The review consists of 1497 publications from 1990 to 2022 (32 years), which reported various opportunities for future performance prediction researchers. First, it evaluates psychological studies, data analysis results, and data mining findings to provide a general picture of the statistical association among students' performance and various influential factors. Second, it critically evaluates new students' performance prediction techniques, modifications in existing techniques, and comprehensive studies based on the comparative analysis. Lastly, future directions and potential pilot projects based on the assumption-based dataset are highlighted to optimize the existing performance prediction systems.

Fabrication and Performance of Low-Fouling UF Membranes for the Treatment of Isolated Soy Protein Solutions

Consumers are becoming more conscious about the need to include functional and nutritional foods in their diet. This has increased the demand for food extracts rich in proteins and peptides with physiological effects that are used within the food and pharmaceutical industries. Among these protein extracts, soy protein and its derivatives are highlighted. Isolated soy protein (ISP) presents a protein content of at least 90%. Wastewaters generated during the production process contain small proteins (8–50 kDa), and it would be desirable to find a recovery treatment for these compounds. Ultrafiltration membranes (UF) are used for the fractionation and concentration of protein solutions. By the appropriate selection of the membrane pore size, larger soy proteins are retained and concentrated while carbohydrates and minerals are mostly recovered in the permeate. The accumulation and concentration of macromolecules in the proximity of the membrane surface generates one of the most important limitations inherent to the membrane technologies. In this work, three UF membranes based on polyethersulfone (PES) were fabricated. In two of them, polyethylene glycol (PEG) was added in their formulation to be used as a fouling prevention. The membrane fouling was evaluated by the study of flux decline models based on Hermia’s mechanisms.